Improving information centrality of a node in complex networks by adding edges

The problem of increasing the centrality of a network node arises in many practical applications. In this paper, we study the optimization problem of maximizing the information centrality $I_v$ of a given node $v$ in a network with $n$ nodes and $m$ edges, by creating $k$ new edges incident to $v$. Since $I_v$ is the reciprocal of the sum of resistance distance $\mathcal{R}_v$ between $v$ and all nodes, we alternatively consider the problem of minimizing $\mathcal{R}_v$ by adding $k$ new edges linked to $v$. We show that the objective function is monotone and supermodular. We provide a simple greedy algorithm with an approximation factor $\left(1-\frac{1}{e}\right)$ and $O(n^3)$ running time. To speed up the computation, we also present an algorithm to compute $\left(1-\frac{1}{e}-\epsilon\right)$-approximate resistance distance $\mathcal{R}_v$ after iteratively adding $k$ edges, the running time of which is $\widetilde{O} (mk\epsilon^{-2})$ for any $\epsilon>0$, where the $\widetilde{O} (\cdot)$ notation suppresses the ${\rm poly} (\log n)$ factors. We experimentally demonstrate the effectiveness and efficiency of our proposed algorithms.Comment: 7 pages, 2 figures, ijcai-201

An investigation on Senior Students’ Behavioral Intention to Use Tencent Meeting for Legal Course in Chengdu, China

Purpose: This research aims to investigate senior students’ behavioral intention to use Tencent meeting for the legal course in Chengdu, China. The key variables are developed from previous literature, including perceived usefulness, attitude, social influence, perceived behavioral control, subjective norm, behavioral intention, and use behavior. Research design, data, and methodology: The target population is 500 fourth-year students at three selected universities who have experience using the Tencent platform for the law course. Probability and nonprobability are used, including judgmental, stratified random, and convenience sampling. Before the data collection, the Item Objective Congruence (IOC) Index and the pilot test (n=30) by Cronbach’s Alpha were assessed to ensure content validity and reliability. Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) were used as statistical tools to confirm validity, reliability, and hypotheses testing. Results: The results show that all hypotheses are supported. Perceived usefulness significantly impacts attitude. Attitude, social influence, perceived behavioral control, and subjective norm significantly impacts behavioral intention. Furthermore, behavioral intention significantly impacts use behavior. Conclusions: Tencent meeting developers, college administrators, or practitioners should focus on improving students’ Tencent meeting use behavior. The developer of Tencent Meeting and the college’s top management should concentrate on making students’ perceptions of the app’s usefulness, social influence, and attitude

Assessment of Behavioral Intention to Use Tencent Meeting of First-Year Students for Legal Courses in Chengdu, China

Purpose: This research aims to assess the behavioral intention to use Tencent meetings of students for legal courses in Chengdu, China. The conceptual framework is developed from previous studies, incorporating perceived usefulness, attitude, social influence, perceived behavioral control, subjective norm, behavioral intention, and use behavior. Research design, data, and methodology: The target population is 500 first-year students at three selected universities who have experience using the Tencent platform for legal programs. The sample methods are judgmental, stratified random, and convenience sampling. Before the data collection, the Item Objective Congruence (IOC) Index and the pilot test (n=30) by Cronbach’s Alpha were assessed to ensure content validity and reliability. Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) were used as statistical tools to confirm validity, reliability, and hypotheses testing. Results: The results show that all hypotheses are supported. Attitude, social influence, perceived behavioral control, and subjective norm significantly impacts behavioral intention and use behavior indirectly. Furthermore, perceived usefulness has a significant impact on attitude. Conclusions: The above key variables should be emphasized and strengthened to improve college students’ use behavior of Tencent meetings in the learning process. Universities ought to pay attention to enhancing a system to maximize students’ learning efficiency